Re: effect size: eta-squared vs partial eta-squared

Cohen's rules of thumb that you list are for mean differences (t-tests,
etc.) translated into r-sq. But his section on correlation uses small
(r = .10, r-sq = .01), medium (r = .30, r-sq = .09), and large (r = .50,
r-sq = .25). He then rectifies these discrepancies on pp. 81-82 (in the
1977 edition, sorry--don't have the '88 at hand, but the chapter on r).

As to your question of partial et-sq and r-sq, I would be curious to any
responses since I always assumed that the interpretation of the effect
size of one would be equivalent to the other.

Re: effect size: eta-squared vs partial eta-squared

A quick search on "eta" in the archives will confirm that some variant of
this question comes up pretty frequently! Unfortunately SPSS still has no
capability to report total eta-squared values or the SS values needed to
calculate total eta-squared values for mixed-model ANOVAs by hand (unless
this functionality has since been added to SPSS 14/15). When Kyle Weeks
last commented on this (Jan 2003 -- see
http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0301&L=spssx-l&P=R2708&m=24876) he said this feature was on the "wish list" but I don't know if it has
since become a reality. If someone knows of development or planned
development on this it would be useful to know about it.

Reporting partial eta-squared values would indeed be misleading given that
when summed they can exceed 1. Unfortunately without SPSS reporting total
eta-squared values it is likely that researchers do erroneously report
partial values.

A quick search on "eta" in the archives will confirm that some variant
of
this question comes up pretty frequently! Unfortunately SPSS still has
no
capability to report total eta-squared values or the SS values needed to

since become a reality. If someone knows of development or planned
development on this it would be useful to know about it.

Reporting partial eta-squared values would indeed be misleading given
that
when summed they can exceed 1. Unfortunately without SPSS reporting
total
eta-squared values it is likely that researchers do erroneously report
partial values.

Re: effect size: eta-squared vs partial eta-squared

Enis, in Keppel and Wickens (2004) the authors make a rather compelling argument against the R^2 effect size as reported in SPSS (i.e, SSa/SStotal) as opposed to omega squared, using their notation on page 164: (SSa - (a -1)MS s/a)/(SStotal + MS s/a)..they comment that omega squared "takes the sampling variability into account and so is most relevant to the population you are studying" (p. 167) and that, in their opinion, r^2 (which statistically can be shown) tends to inflate the variation accounted for.....however, SPSS does not report omega squared so one would need to hand calculate this estimate

"Dogan, Enis" <[hidden email]> wrote:
Thanks to all who replied to my question.
Any comments on the relationship between partial-eta squared and partial
R squared?
Also eta-squared looks to me like a part R squared.
Any thoughts?

A quick search on "eta" in the archives will confirm that some variant
of
this question comes up pretty frequently! Unfortunately SPSS still has
no
capability to report total eta-squared values or the SS values needed to

since become a reality. If someone knows of development or planned
development on this it would be useful to know about it.

Reporting partial eta-squared values would indeed be misleading given
that
when summed they can exceed 1. Unfortunately without SPSS reporting
total
eta-squared values it is likely that researchers do erroneously report
partial values.

Re: effect size: eta-squared vs partial eta-squared

Hi

DG> Enis, in Keppel and Wickens (2004) the authors make a rather
DG> compelling argument against the R^2 effect size as reported in
DG> SPSS (i.e, SSa/SStotal) as opposed to omega squared, using their
DG> notation on page 164: (SSa - (a -1)MS s/a)/(SStotal + MS
DG> s/a)..they comment that omega squared "takes the sampling
DG> variability into account and so is most relevant to the population
DG> you are studying" (p. 167) and that, in their opinion, r^2 (which
DG> statistically can be shown) tends to inflate the variation
DG> accounted for.....however, SPSS does not report omega squared so
DG> one would need to hand calculate this estimate

SPSS vs. SAS for n = 1 at level 1 for mixed model

I have a multilevel dataset with quite a few singletons, i.e, level one units with n = 1; I believe that all of the level 2 units will be kept for the fixed effects analysis, but this would not be the case for the estimation of the variance components. A colleague commented that in regards to SAS MIXED....."SAS probably addresses this issue by excluding "effects" (which are confounded across levels) when estimating sample- and group-level variances". However, I use SPSS mixed model and HLM6.0 and I wanted to see if anyone on this listserv knew if SPSS and/or HLM handle singletons (n =1 at level 1) statistically in the same way as SAS?....thank you very much for any help......Dale